Open Access

Characterization of novel SSR markers in diverse sainfoin (Onobrychis viciifolia) germplasm

  • Katharina Kempf1,
  • Marina Mora-Ortiz2,
  • Lydia M. J. Smith2,
  • Roland Kölliker1 and
  • Leif Skøt3Email author
Contributed equally
BMC GeneticsBMC series – open, inclusive and trusted201617:124

https://doi.org/10.1186/s12863-016-0431-0

Received: 13 April 2016

Accepted: 22 August 2016

Published: 30 August 2016

Abstract

Background

Sainfoin is a perennial forage legume with beneficial properties for animal husbandry due to the presence of secondary metabolites. However, worldwide cultivation of sainfoin is marginal due to the lack of varieties with good agronomic performance, adapted to a broad range of environmental conditions. Little is known about the genetics of sainfoin and only few genetic markers are available to assist breeding and genetic investigations. The objective of this study was to develop a set of SSR markers useful for genetic studies in sainfoin and their characterization in diverse germplasm.

Results

A set of 400 SSR primer combinations were tested for amplification and their ability to detect polymorphisms in a set of 32 sainfoin individuals, representing distinct varieties or landraces. Alleles were scored for presence or absence and polymorphism information content of each SSR locus was calculated with an adapted formula taking into account the tetraploid character of sainfoin. Relationships among individuals were visualized using cluster and principle components analysis. Of the 400 primer combinations tested, 101 reliably detected polymorphisms among the 32 sainfoin individuals. Among the 1154 alleles amplified 250 private alleles were observed. The number of alleles per locus ranged from 2 to 24 with an average of 11.4 alleles. The average polymorphism information content reached values of 0.14 to 0.36. The clustering of the 32 individuals suggested a separation into two groups depending on the origin of the accessions.

Conclusions

The SSR markers characterized and tested in this study provide a valuable tool to detect polymorphisms in sainfoin for future genetic studies and breeding programs. As a proof of concept, we showed that these markers can be used to separate sainfoin individuals based on their origin.

Keywords

Onobrychis viciifolia Sainfoin Microsatellite SSR Genetic diversity Molecular markers Fingerprinting

Background

Onobrychis viciifolia Scop., commonly known as sainfoin, belongs to the tribe Hedysareae and the family Fabaceae. It is a tetraploid (2n = 4x = 28) perennial forage legume, rich in proteins and secondary plant metabolites. Its center of origin is attributed to the Middle East and Central Asia. It was introduced into Europe in the fifteenth century and was rapidly adopted by farmers due to its high fodder value, especially for working horses [1]. Nowadays, sainfoin is cultivated only in small areas for fodder production and on ecological compensation areas. Its cultivation steadily declined since the 1950’s, due to the expanding availability of inorganic fertilizers and the preference for higher yielding legume crops such as alfalfa (Medicago sativa) or red clover (Trifolium pratense) [24]. In the last few years, however, sainfoin has gained renewed interest due to its animal health promoting properties associated with the presence of condensed tannins (CT) and other complex phytochemicals in the foliage. Benefits include anthelminthic properties and prevention of potentially lethal bloat associated with most other forage legumes [59]. In addition, sainfoin shows a range of beneficial agronomic features. In common with most other legumes sainfoin fixes atmospheric nitrogen in its root nodules, thus reducing the need for industrial N fertilizer input. [1, 10]. Furthermore, soil fertility is improved by increased humus development through its deep rooting capability and low input requirements once established [11]. Used as a component of permanent grassland, sainfoin is a valuable alternative for areas suffering from intensification, as it increases soil fertility and has become a popular addition to non-cropped environmental planting; sainfoin provides good resources for native insects and high quality fodder for livestock [11].

Despite its advantages, a wide distribution of sainfoin is hampered by the often poor agronomic performance and the lack of sainfoin varieties adapted to different environmental conditions. The main weaknesses of sainfoin lie in its low tolerance to waterlogging and frost as well as in its poor competitive ability in the early stages of development. Therefore, targeted breeding activities are needed to select for sainfoin individuals better adapted to a broad range of environmental conditions. Breeding activities have also been impaired by the lack of knowledge of the genetic diversity of the species and its mode of inheritance. Further investigation and development of tools for marker assisted breeding has been hampered by the limited availability of species-specific molecular markers. So far, most studies have focused on the use of cross-amplifiable EST-SSRs, mainly from Medicago and Glycine species; ITS markers based on nuclear internal transcribed spacer regions and dominant SRAP markers [1215]. The use of co-dominant SSR markers developed in other species yielded only a low number of alleles per locus in sainfoin (from 5 to 7 in bulks of 10 individual plants [12]. The development of highly informative, specific markers for sainfoin is indispensable to create a genetic knowledge base and assist breeding by marker assisted selection (MAS) [16].

SSRs or Microsatellites [17] are composed of tandemly repeated sections of DNA [18]. SSR markers show co-dominance of alleles and are randomly distributed along the genome, particularly in low-copy regions [19, 20]. Considering the complex tetraploid sainfoin genome and the lack of knowledge about its genetics, SSRs are the markers of choice. SSR are multi-allelic in contrast to next generation high-throughput sequencing (NGS) derived SNP marker which are bi-allelic. This makes SSR markers highly variable and useful for distinguishing even between closely related populations or varieties [21]. Furthermore, SSR are easily detected using standard PCR methods and are transferable to related taxa [22]. The development of NGS has recently enabled the identification of a large set of set of SSR sequences from sainfoin (Mora-Ortiz et al. 2016, BMC Genomics, accepted).

In this study, our aim was to develop and characterize a comprehensive set of markers based on recently identified SSR sequences (Mora-Ortiz et al. 2016, BMC Genomics, accepted) in a panel of 32 sainfoin individuals of different origin.

Methods

Plant material

In order to include a large range of genetic diversity, we selected a set of 32 individual sainfoin plants from 29 different accessions (Table 1), originating from a range of geographical regions and showing differences for tannin content and composition [12, 13, 23, 24]. These accessions were grown in the glasshouse at the National Institute of Agricultural Botany (NIAB) (Cambridge UK) and in the field at Agroscope (Zurich, Switzerland). Young leaf material was collected from each single plant, ground in liquid nitrogen and stored at −80°C until subsequent DNA extraction.
Table 1

O. viciifolia individuals used for marker characterization in this study

Individual number

Variety

Status

Origin

Source

ID_01

247

NA

Morocco

GRIN

ID_02

Buceanskij

NA

Romania

GRIN

ID_03

CPI 63750

NA

Turkey

GRIN

ID_04

CPI 63764

wild

Turkey

GRIN

ID_05

CPI 63820

NA

Spain

GRIN

ID_06

CPI 63826

NA

Spain

GRIN

ID_07

NA / RCAT028437

NA

Hungary

GRIN

ID_08

Ökotyp Wiedlisbach

ecotype

Switzerland

ISS

ID_09

Premier

landrace

Switzerland

ISS

ID_10

Rees A

cultivar

UK

GRIN

ID_11

TU86-43-03

cultivated

Turkey

GRIN

ID_12

Nova

cultivar

Canada

GRIN

ID_13

Visnovsky

cultivar

Czech Republik

ISS

ID_14

Perly

cultivar

Switzerland

ISS

ID_15

Brunner

landrace

Austria

ISS

ID_16

Perdix

cultivar

Switzerland

ISS

ID_17

Cotswold Common

cultivar

UK

RAU

ID_18

Perly

cultivar

Switzerland

RAU

ID_19

Somborne

cultivar

UK

RAU

ID_20

Ibaneti/ RCAT028292

NA

Romania

RCAH

ID_21

Bivolari/RCAT028294

cultivar

Romania

RCAH

ID_22

NA/170582

NA

Hungary

RCAH

ID_23

CPI 637554/ 192995

NA

Turkey

GRIN

ID_24

CPI 63767 / 212241

cultivar

USA

GRIN

ID_25

Na/228352

wild

Iran

GRIN

ID_26

CPI 63781/ 236486

NA

Turkey

GRIN

ID_27

Cholderton Hamshire Common

cultivar

UK

GRIN

ID_28

Visnovsky

cultivar

Czech Republic

GRIN

ID_29

Zeus

cultivar

Italy

Cotswold Seeds Ltd

ID_30

Zeus

cultivar

Italy

Cotswold Seeds Ltd

ID_31

Ambra

cultivar

Italy

private

ID_32

Esparcette

cultivar

UK

private

RAU Royal Agricultural University Gloucestershire UK, RCAH Research Centre for Agrobotany Tápiószele; Hungary, GRIN Germplasm Resources Information Network, Washington, USA, ISS Agroscope Institute for sustainability science, Zurich, Switzerland

DNA extraction

DNA was extracted using the Nucleon Phytopure Genomic DNA extraction kit (GE Healthcare, Little Chalfont Buckinghamshire, United Kingdom) following the manufacturer’s instructions. This method has been shown to be suitable for extraction of high quality DNA from O. viciifolia, in which high levels of polyphenol and condensed tannins have been reported to interfere with a successful DNA extraction using other approaches [14]. DNA quality and quantity was assessed using gel electrophoresis and spectrophotometry.

PCR and Gel electrophoresis

A total of 400 SSR primers designed from O. viciifolia transcriptome data (Mora-Ortiz 2016, unpublished), were tested with unlabeled primers for amplification in the 32 plants using an iCyler (Biorad, Hercules, USA) in a volume of 10 μL, with 10 ng DNA, 1 x Go Taqflexi buffer (Promega, Madison, USA), 2.5 mM MgCl2 (Promega), 0.2 mM dNTPs (Promega), 0.2 μM forward primer, 0.2 μM reverse primer and 0.5 U Polymerase G2 (Promega). The conditions followed a touchdown PCR approach with 4 min at 94 °C, 12 cycles of 30 s at 66 °C with −1 °C decrease at each cycle plus 30 s at 72 °C, and 30 cycles of 30 s at 94 °C, 30 s at 54 °C plus 30 s at 72 °C, followed by 7 min at 72 °C. PCR products were separated by gel electrophoresis. Amplicons were separated on 1 % agarose in 1x TBE buffer, stained with ethidium bromide and visualized under UV light.

M13 PCR and capillary electrophoresis

Those 101 primer pairs that successfully amplified fragments in the 32 individuals (Table 2) were further characterized for polymorphisms using the M13 (–21) tail primer genotyping protocol [25]. The PCR reactions were conducted in an iCycler (Biorad) in a sample volume of 10 μL, each containing 20 ng DNA template, 1x Go Taqflexi buffer (Promega), 1.5 mM MgCl2, (Promega), 0.2 mM dNTPs (Promega), 0.16 μM forward primer carrying the M13-tail, 0.04 μM reverse primer and 0.16 μM fluorescently labelled M13-primer, 0.5 U polymerase GoTaq G2 (Promega).
Table 2

SSR primer sequences used for amplification in 32 O. viciifolia individuals and characteristics of SSR motifs

Marker

Motif

Repeats

Predicted size

Forward primer (5′–3′)

Reverse primer (5′–3′)

OVK002

AG

9

164

CCCACCAGACAAAAAGAATA

GCTTTCCCCTTCATCAACTAT

OVK003

TA

8

122

GATAGAATTCGTTTGTTGGTG

ATCTTTGTAACTGTTCGCTCA

OVK017

AC

8

158

GGGTGTTAGTTATCCATTTCC

ACATACTAGCCTTCTGGGGTA

OVK027

CTCG

6

129

AATGGAATCTCGGAGACAG

GGAAGAAGACGAAGTAGTAGGA

OVK034

GCT

6

150

GTGAGATGAGCTTGGACATT

AGATAACTAACTGCAGGCAAG

OVK036

AGGT

6

150

GTGTTAAAGGGGTGAAAACAT

CATTTTGACAAACCAGTATCC

OVK038

ATT

6

166

CCACATACGAGACAGAATAGG

CTGAAAATTGATCGATACTGG

OVK042

GTT

6

144

GGAACGGTTAATTTCTGATTT

AGAATTCCGTACAAGTCGAG

OVK045

AGA

6

148

CCAAAAATCATCAATCAACAC

TTGAACAAGGGTTAGGGTTAT

OVK046

AGTG

6

151

TCAACCACATTATAAAACCTCA

CGCGAAATCATAGTTCACTT

OVK054

GAA

6

201

TTGCAGAGATAACACTCACCT

TCCTGAAAAACCTAATCACAA

OVK055

GAT

6

189

GAAGATATTTCAAAGCAGCAA

CATGCTACCACTAGCAGAAGT

OVK063

TTG

6

188

AATTGCAACTGAAACTGAAAC

ACTGCTACCCTCTCCATAAAT

OVK068

GGA

6

195

GACCACCCGCAGCTCAAC

GTCTTCTTCCCCCATATTTAG

OVK072

ACC

7

199

TTGCCTTAGTCAGTTACCTTG

GTGGAGAGAATGAGAGAACCT

OVK073

GAC

6

200

GTAGACAACCGTATCTGGACA

AAGATGGAAGGTTCTAGTTCG

OVK077

TTA

6

249

GTCCCTCTCTCTCAAATTGTT

AGGTTAATGGAGCTTAGTGGT

OVK089

CAT

6

257

CAAAGTCATACCAATCACCAT

TCTTGGAAGCACTTGTTACTC

OVK093

CCA

6

259

CCAAGTGTTTGAAAGTCTCAG

TGAGAGTTCGTTCAAGGTAGA

OVK094

TTGCG

5

255

ACCGATCTTAGGATAGATGGA

ACTTTTGGTTGCTTAGTCGAT

OVK096

TCA

6

249

GAGCGTTGCATTTACATTTAC

CATCCTCCTTTACACCCTAAT

OVK097

GTGA

6

252

TCTATAGAGATGAGGCGACAA

CGCCCCTAACTAACCTACTAC

OVK099

TGAG

6

247

AGAAAATGGAAGCAACAGAGT

ACAAATAGCAGCTCCCTTC

OVK101

CTAA

6

254

GTTGAGTTTCAGACACAGAGC

AATAGCTCCCACAATAACTCC

OVK102

TGT

6

249

CCAAAGGGTGTTTTATTTTCT

GGAAGAAATTAAGCAAATGGT

OVK107

AG

8

193

AAGTTAAAACTTTGCGTTGTG

GACGTTGTTCTGGATTTCTTC

OVK111

GGT

7

206

TATAGACCTTCTCCTCCCAGA

GTGAAAGTCACAAATCCAAAG

OVK119

CAG

6

199

ACCCTCCTTCTCTCCTTATTT

GACGAGAGAACTCGTTTATGA

OVK122

TC

9

211

GCAGATAGCACAGTTATCGAC

GAACCACACACACAGAATCA

OVK123

ACA

9

200

CACCCATTAACTATCATGGTC

CAAGCCCTTTGTGAGATACTA

OVK124

TGA

9

211

GCCTTTTCTGTGACTCGTAA

GCTCCATTCCCATTTATAGTT

OVK125

CATTT

5

193

AAATTTAAGCACCGGAATAAC

AAAGCAAAAGGGCTACTAAAG

OVK126

TC

8

197

CGACAAAACTATTTAGGCAAA

GGGAAGAGATCATAAACCCTA

OVK127

AT

8

200

GCCCAAAATGTATTATCCTTC

AGAACAGACAGATATGCAAGC

OVK131

TA

8

200

TCTATCTGGGTGTTGTTTTGT

CTGTTTGAATATCGATTACCAC

OVK133

TG

8

196

TGCTTCAGCATTATTGTAACAT

TGCACTTCTCCATACTTCCTA

OVK138

CTAA

6

250

TAATATGGTGCAAGTTCCAAT

TTCTACGCTTAGCTCAAACC

OVK141

CACG

6

239

GAGGAGGTACATACAGCACAG

CAACCTCCTCGTTATCTTTTT

OVK142

GT

8

243

AACATGACTACTGTGAACAAGG

CGAACATGTAATTGATCCAAG

OVK155

GTG

6

251

CAGGTTTGAAGTAGCAGAGAA

GTAGACCACGCATACTGAATC

OVK158

GACT

6

257

TCAGAGTGTGTTGTGTTGTGT

AGTGAAGCAAATGTGTGATTT

OVK159

TG

8

251

CATTATTGCCTAGCATTGTTC

ATTTCACCATCAAGTATGCAC

OVK161

TTCC

6

249

AAAGCTTTCTACACGTTGGTA

TGGGTTTTTACACTCTGTGAT

OVK165

ACA

6

267

TTTCAAACACTCACTCACTCC

TCGGATTTGTGACCTAACTC

OVK168

TGA

6

253

AATTATCACCCACTGCTATGA

GGTTTCCATCACTGTTTGTTA

OVK172

AGC

6

256

TTATTAAACCTGCGTCTTCTG

GTAGAGCTGTGGGCTTTATCT

OVK173

CT

8

253

TCGTTCTCGTGATTATTCTGT

CCTCTATTCAAATAGGGCAAT

OVK174

GGCCC

5

246

ACATGATCGTGAATATGAAGC

CAGCAGCAATCAATATATCATC

OVK175

CA

9

250

GTAAAATATCAAGCAGGAGCA

AAACTATGCAGACACCCTGTA

OVK177

CTG

7

257

TCTGTTGATTTAAGGAGACGA

CTCTTGCTCATATTTTCCTCA

OVK181

AAG

6

257

AGGAAGAAGAAGAAGAAGCAG

TTCTCCTTTAACCACAACCTT

OVK183

TGAT

6

256

GAGGGTAAGAGAGAGTGGAAG

CTTGCCTGATATCTTCTCAAA

OVK196

AGC

6

286

TTTTGAGAGTGTGGAAGGTTA

AGTATGAGCCTGATGATGATG

OVM003

TC

9

297

CCGTCTGTTTAATCATTCACT

GAAAGGAAAGGTTATTGGAGA

OVM004

ATTT

5

290

GGGAATTCTTAAATCTCATGG

ATGCATGGTACTGGGTCGT

OVM025

CAA

6

297

TTCTGAACAACAACAACAACA

GTCCAGGAGCTAAGTAACCAT

OVM031

TGA

6

306

ATTGGTTTCTAAGGAGGACTG

GCAATACTCCTCTGCCTAGTT

OVM033

CTC

6

300

CAAGGCTTATTTGGTTAACAG

ATACTATTTCCCATGCCTACC

OVM034

TTC

6

308

GCATTTCATCAAACACTTTTC

TTGGTTTGAATCTGTGAGACT

OVM035

TTC

7

303

TCATCAAACACTTTTCGTTCT

TTGGTTTGAATCTGTGAGACT

OVM038

GAAG

6

297

CACAGGACAAGAGTGAGAGAG

TCATGATACCACGAATTTTTC

OVM043

GAG

7

167

TAGTATGGCTGAAATCAAAGG

ATATCATAAGGGCAACAGTGA

OVM048

AT

9

157

GACATTGAAATCAAACAATCC

AACACTTGTCATGTTTCCAAG

OVM049

TGA

7

150

AACAAACAAGAGGAAAAGGAG

TATGTGCTTATCAGGCATTTT

OVM050

ATCC

6

161

ATGAGCATGAAGAGTTTCAGA

ACACATCTACGACTTCTTTCG

OVM053

GTGGA

5

149

CACCAAAAGCATAGCAATAGT

GCTTGAATTGAATGAGAAATG

OVM057

TTG

6

153

CCTTGAGGAGGAATAATAGGA

GACATCATCATCACCTTCACT

OVM058

AT

9

150

GTCAAGTCATACCCATACGAG

CAGTGTAACCATATGCACAAA

OVM059

AGA

6

149

ACTCCAACTCCAACTCAGAAC

AAGCGAAGAAGAGAGTGAGAT

OVM060

CT

8

159

ATGTAATCAAAAGGTGCAGAA

AGCTTCCAAAACAGTGTATGA

OVM061

GTA

6

150

TTAACACACGTACGTACCACA

TTTGTCGTTGATCGTTAAGTT

OVM062

AG

8

139

GGAAAAAGGTTTGGATAGATG

AAGTTTTCCCCACACTATTCT

OVM064

AT

8

353

GCATGCACAGAATTAAGTTTC

AGAAGGTCCTTTGAAAATCAG

OVM065

CT

8

352

AAGACAGCGAGTTACCAATCT

GATTGAAACTGAGTAGCGATG

OVM067

CTT

6

352

CAACCTTAATACCAACCTTCC

AAAAGTAGCCAGAGAGCAAAT

OVM068

CCT

6

333

CTACAACTCACCGAAACTCAC

CGATTTCTGCCTCTTTATTCT

OVM069

AATG

6

357

ATGTTGTACAGATGAGCTTCG

TAGTGAGCAAACCTATTTTGG

OVM072

GAA

6

350

TTGATGTGGTTGATCCTATTC

GATGTCAACATCTTGGTCATTA

OVM073

ACA

6

346

GTTCTCAAACGCACTATCAAC

AAAATCTTGTAGGGATTCGAT

OVM076

AAC

7

348

CCCATTCTTCATCTTTCTCTC

TGCTTCCATAATCAGTGAAAT

OVM081

GT

9

350

TCTAGCACAATGTTTTGGATT

TATTGAGTTGAAGCAGACCAT

OVM083

CT

8

347

CACACAAACACAAAACTCACA

GATCGGAGAAAAGAAGAAGAG

OVM086

GAA

6

350

TCATACAAAGTTCCTTCCGTA

ATTGCCAATAACAGTGAAGAG

OVM090

CCA

6

151

AATCAATGGAGGAGGATAAAC

GAAGGTTGAAAAGGGAATAAA

OVM091

ATC

6

188

AACCACCCTTAATTCCATAAG

AGATAAAAGCCGCAAAAGTAT

OVM092

CAC

6

157

GGACCAACAAAGAGGATTATT

CCCTTGCTTGAAGTGTTACTA

OVM094

GTTT

5

163

ATTCATGGGGACAATAAATTC

CAAGAGAATGAATGAATCAGC

OVM099

GA

9

149

TATGTATTGCAGAATCACAGC

TATTACCCTTTTCCATCTTCC

OVM100

AAG

6

151

GAACTAGATTTGCGGCATT

CCCACACCCTTATCCTTATTA

OVM110

AT

8

154

CTGGACGAAAACAACATATTC

GTTGGCTTTGGTACTGACATA

OVM116

GAT

7

151

AACTACACGCACGTAATGAAT

TGGTTTGATAAACACCTCAAG

OVM120

TTC

6

152

TTCAGTGTCACTTTCCTCATT

AGAAGTTGTCATGTCAAGGAA

OVM122

TGG

6

156

ATGAATCTTGTACGGAATCTG

GAAGAAAAAGCCATAAACACC

OVM125

AAATT

5

151

ATTCTTTCAACAAGCAAGTGA

CTGCAATTCCATCCTATTTTA

OVM126

TCC

6

188

ACTAAGAACCACCCAAAACAT

TGAGAAGATGGAGAAGATGTG

OVM128

TGTT

6

155

GAGAAGCATAACCAAAATCCT

TGGAAGAAAAGAAACTTCTGA

OVM129

TG

8

133

AATTGGATTCATGTGTTAGGA

GAAGTGGAGCCAAAACCT

OVM130

AG

9

154

GCAAATTATCACCATGCAC

CGTGAAGAAAATCGGTACTTA

OVM131

AGA

6

153

GAAATAACGCAGGCAGATAC

AATTAGAGGCTTCGACTTGTT

OVM132

GAC

6

142

ACGGTAATCAGTAGTGACAGC

GTGTGACAGAAAATGGGATTA

OVM133

TTTC

5

171

TAGCATCAAGGTTGGAAATAG

CTAGGCTACCTGAATCAAACA

PCR conditions were 2 min at 94 °C, 30 cycles of 30 s at 94 °C, 45 s at 56 °C and 45 s at 72 °C, followed by 8 cycles of 30 s at 94 °C, 45 s at 53 °C and 45 s at 72 °C. The final extension step was conducted at 72 °C for 10 min. An aliquot of 1μl of the PCR product was diluted in 10 μl HiDi™ formamide (Applied Biosystems®, Thermo Fisher Scientific, Waltham, MA, USA) and 0.2 μl Rox 500™ oligonucleotide ‘size ladder’(Applied Biosystems®) for capillary electrophoresis on the Genetic Analyzer 3730 (Life Technologies, Carlsbad, CA). Alleles were scored using the GeneMarker software (Softgenetics, V2.4.0 Inc., State College, USA).

Data analysis

All statistical analyses and calculations were performed using R statistical software (R Core Team, 2014). The polymorphism information content (PIC) of SSR markers was calculated as the mean of the PIC of each allele, using the formula for dominant markers from Roldan-Ruiz et al. [26] as;
$$ \mathrm{PI}{\mathrm{C}}_{\mathrm{i}} = 2{\mathrm{f}}_{\mathrm{i}}\left(1\ \hbox{-}\ {\mathrm{f}}_{\mathrm{i}}\right), $$

where PICi is the polymorphism information content of allele i and fi is the frequency of occurrence of allele i (fragment present) in the 32 individuals. From single alleles, average (PICAv), minimum (PICMin) and maximum (PICMax) PIC values were calculated for each SSR marker.

In order to calculate genetic distance measures, SSR alleles were coded as individual markers with 1 for presence and 0 for absence of the allele as binary data. Pairwise genetic distances between individuals were calculated as modified Rogers’ distance Dw, [27] which shows the extent of genetic diversity between two individuals [28] ranging from 0 (no diversity between individuals) and 1 (maximum diversity).

Genetic relationships were visualised using cluster analysis and the R-function pvclust() [29] based on Euclidean distance that was rescaled to Dw for plotting purposes (Dw and Euclidean distance show a linear relationship, Additional file 1: Figure S1). Probability values (p-values) were calculated for each cluster using multiscale bootstrap resampling [30, 31] to calculate approximately unbiased (AU) p-values [32]. The k-means clustering algorithm [33] was applied to the Dw values using a sequence of k = 2 clusters to 32 clusters. The Calinsky criterion [34] was then calculated for each number of k as implemented in the R function cascadeKM() and the optimum number of clusters was determined at the maximal value. Population structure was further investigated by principal component analysis performed on binary raw data of individual alleles.

Results

SSR analysis

SSR markers showed a high degree of polymorphism and overall, 1154 alleles were found with an average of 11.4 alleles per marker locus (Table 3). Among those 1154 alleles, only five alleles (from SSR OVK042, OVK172, OVM031, OVM072 and OVM100) were non-polymorphic and hence present in all individuals studied.
Table 3

Characterization of the 101 polymorphic sainfoin markers

Marker

PIC Av

PIC Min

PIC Max

NoA

NoA Priv

MinAF

MaxAF

Size

OVK002

0.22

0.06

0.47

9

3

0.03

0.63

154–175

OVK003

0.23

0.06

0.47

11

2

0.03

0.38

92–124

OVK017

0.22

0.06

0.50

19

5

0.03

0.47

148–184

OVK027

0.28

0.06

0.50

9

2

0.03

0.59

120–140

OVK034

0.27

0.06

0.49

12

1

0.03

0.56

138–154

OVK036

0.35

0.17

0.50

7

0

0.09

0.69

133–154

OVK038

0.19

0.06

0.40

14

4

0.03

0.28

155–186

OVK042

0.25

0.00

0.50

2

0

0.50

1.00

183–186

OVK045

0.29

0.12

0.43

6

0

0.09

0.94

138–148

OVK046

0.31

0.06

0.49

12

1

0.03

0.56

138–157

OVK054

0.29

0.12

0.49

15

0

0.06

0.44

274–290

OVK055

0.20

0.06

0.38

8

2

0.03

0.84

135–159

OVK063

0.24

0.06

0.50

13

2

0.03

0.72

179–200

OVK068

0.25

0.06

0.43

9

3

0.03

0.31

186–213

OVK072

0.32

0.12

0.50

4

0

0.06

0.81

193–198

OVK073

0.29

0.06

0.50

11

1

0.03

0.53

186–210

OVK077

0.23

0.06

0.45

9

2

0.03

0.78

233–264

OVK089

0.27

0.06

0.49

9

2

0.03

0.44

279–299

OVK093

0.23

0.06

0.50

14

6

0.03

0.56

234–271

OVK094

0.24

0.06

0.48

14

4

0.03

0.66

208–244

OVK096

0.21

0.06

0.48

20

6

0.03

0.41

215–294

OVK097

0.22

0.06

0.38

3

0

0.13

0.97

240–248

OVK099

0.25

0.06

0.49

13

2

0.03

0.75

232–270

OVK101

0.36

0.06

0.50

7

1

0.03

0.72

339–352

OVK102

0.23

0.06

0.34

4

1

0.03

0.22

239–251

OVK107

0.29

0.06

0.45

15

1

0.03

0.72

206–234

OVK111

0.26

0.06

0.48

7

2

0.03

0.75

213–232

OVK119

0.30

0.06

0.47

10

1

0.03

0.72

216–252

OVK122

0.24

0.06

0.45

8

1

0.03

0.66

330–341

OVK123

0.26

0.06

0.50

10

3

0.03

0.75

208–237

OVK124

0.26

0.06

0.49

15

1

0.03

0.44

218–267

OVK125

0.29

0.06

0.50

9

1

0.03

0.72

197–222

OVK126

0.25

0.06

0.49

15

3

0.03

0.56

198–233

OVK127

0.28

0.06

0.49

6

1

0.03

0.44

204–222

OVK131

0.17

0.06

0.48

15

3

0.03

0.59

183–228

OVK133

0.25

0.06

0.50

13

3

0.03

0.63

205–239

OVK138

0.21

0.06

0.49

13

6

0.03

0.56

232–267

OVK141

0.14

0.06

0.47

15

7

0.03

0.38

242–269

OVK142

0.25

0.06

0.50

12

3

0.03

0.47

256–285

OVK155

0.24

0.06

0.49

14

3

0.03

0.56

234–282

OVK158

0.19

0.06

0.40

24

6

0.03

0.28

273–375

OVK159

0.23

0.06

0.50

14

4

0.03

0.81

268–290

OVK161

0.25

0.06

0.40

12

1

0.03

0.28

220–276

OVK165

0.19

0.06

0.50

20

6

0.03

0.50

273–311

OVK168

0.24

0.06

0.50

11

3

0.03

0.81

258–284

OVK172

0.16

0.00

0.30

5

0

0.06

1.00

268–279

OVK173

0.23

0.06

0.48

18

5

0.03

0.59

268–316

OVK174

0.23

0.06

0.48

5

2

0.03

0.75

245–266

OVK175

0.19

0.06

0.38

10

2

0.03

0.88

252–267

OVK177

0.27

0.06

0.49

7

2

0.03

0.59

267–286

OVK181

0.19

0.06

0.38

19

4

0.03

0.25

343–381

OVK183

0.24

0.06

0.49

17

1

0.03

0.44

266–289

OVK196

0.21

0.06

0.50

8

2

0.03

0.53

297–314

OVM003

0.31

0.12

0.47

10

0

0.06

0.69

299–321

OVM004

0.21

0.06

0.45

18

6

0.03

0.34

380–426

OVM025

0.33

0.17

0.49

7

0

0.09

0.84

306–324

OVM031

0.26

0.00

0.50

13

1

0.03

1.00

292–353

OVM033

0.29

0.06

0.50

8

1

0.03

0.69

308–330

OVM034

0.22

0.06

0.50

17

5

0.03

0.53

307–355

OVM035

0.22

0.06

0.50

17

5

0.03

0.53

301–350

OVM038

0.19

0.06

0.43

14

4

0.03

0.31

311–351

OVM043

0.30

0.06

0.49

10

2

0.03

0.66

173–203

OVM048

0.29

0.12

0.43

6

0

0.06

0.72

174–186

OVM049

0.31

0.06

0.50

9

1

0.03

0.50

162–198

OVM050

0.20

0.06

0.49

13

6

0.03

0.72

168–198

OVM053

0.32

0.06

0.50

11

1

0.03

0.50

134–182

OVM057

0.35

0.17

0.49

5

0

0.09

0.66

165–180

OVM058

0.23

0.06

0.49

15

3

0.03

0.44

135–178

OVM059

0.24

0.06

0.48

7

2

0.03

0.59

156–174

OVM060

0.23

0.06

0.50

21

4

0.03

0.50

172–219

OVM061

0.19

0.06

0.50

10

4

0.03

0.84

143–175

OVM062

0.30

0.06

0.49

12

2

0.03

0.59

151–170

OVM064

0.16

0.06

0.47

16

8

0.03

0.38

380–444

OVM065

0.25

0.06

0.49

14

4

0.03

0.69

360–391

OVM067

0.33

0.17

0.49

6

0

0.09

0.66

366–380

OVM068

0.26

0.12

0.43

8

0

0.06

0.88

343–368

OVM069

0.26

0.06

0.48

13

2

0.03

0.59

454–479

OVM072

0.28

0.00

0.50

7

1

0.03

1.00

365–387

OVM073

0.20

0.06

0.45

21

5

0.03

0.34

446–511

OVM076

0.22

0.06

0.48

17

3

0.03

0.41

347–376

OVM081

0.18

0.06

0.40

17

5

0.03

0.28

353–396

OVM083

0.30

0.06

0.50

11

2

0.03

0.63

365–384

OVM086

0.32

0.06

0.50

10

1

0.03

0.63

371–391

OVM090

0.30

0.06

0.49

8

2

0.03

0.84

158–180

OVM091

0.34

0.06

0.50

8

1

0.03

0.47

184–217

OVM092

0.23

0.06

0.43

7

2

0.03

0.81

163–185

OVM094

0.33

0.12

0.50

7

0

0.06

0.81

190–207

OVM099

0.18

0.06

0.50

11

5

0.03

0.50

165–198

OVM100

0.24

0.00

0.48

5

0

0.09

1.00

163–179

OVM110

0.21

0.06

0.49

18

5

0.03

0.44

163–185

OVM116

0.28

0.06

0.49

15

2

0.03

0.56

138–204

OVM120

0.34

0.06

0.50

6

1

0.03

0.88

169–187

OVM122

0.31

0.06

0.50

4

1

0.03

0.91

164–180

OVM125

0.26

0.06

0.50

10

3

0.03

0.47

161–180

OVM126

0.22

0.06

0.50

18

4

0.03

0.53

191–229

OVM128

0.26

0.06

0.47

9

1

0.03

0.81

173–190

OVM129

0.25

0.06

0.49

14

4

0.03

0.56

146–173

OVM130

0.20

0.06

0.47

20

7

0.03

0.38

152–187

OVM131

0.34

0.06

0.50

8

2

0.03

0.56

159–198

OVM132

0.30

0.06

0.47

8

2

0.03

0.78

157–176

OVM133

0.20

0.06

0.47

14

3

0.03

0.63

177–212

PICAv, PICMin and PICMax give the average, minimum and maximum allele-wise polymorphism information content values, NoATot the total number of alleles, NoAPriv the number of private alleles, MinAF the minimum allele frequency and MaxAF the maximum allele frequency value

With only two alleles in the 32 individuals, SSR OVK042 had the lowest number of alleles, whereas OVK158 had the highest number with 24 amplified alleles. The minimum rate of allele occurrence was 0.03125, corresponding to occurrence in only one genotype (i.e. a private allele of an individual genotype). In total, 250 private alleles were detected and these were equally distributed across the examined set of individuals and markers. With regard to individuals, the highest number of private alleles over all markers was found for individual ID_08 (14 private alleles) and the lowest number was found for ID_17 (3 private alleles). The origin of the individual did not appear to affect the occurrence of private alleles. With regard to markers, the most private alleles were observed in OVM064 (8 private alleles), whereas 16 markers (15.8 %) had no private alleles at all.

The average polymorphism information content (PICAv) ranged from 0.14 (OVK141) to 0.36 (OVK101) (Table 3). A detailed look at the PIC values of individual alleles in the different markers exhibited minimum PIC values per SSR (PICMin) between 0 (Additional file 2: Figure S2), OVK042, OVK172, OVM031, OVM072, OVM100) and 0.17 (OVK131) and maximum PIC values per SSR (PICMax) between 0.3 (OVK 172) and 0.5 (16 different markers).

The overall length of SSR fragments detected ranged from 91 to 511base pairs (bp). Markers with two base pair motifs had a slightly higher number of repeats (eight to nine) when compared to markers with three to five bp motifs (five to seven repetitions). The total fragment length observed did not differ between motif lengths (data not shown). Contrastingly, the number of alleles found for SSRs with two bp motifs was higher (13.5 alleles on average), compared to SSRs with longer motifs (10.7 alleles). The average number of alleles per sainfoin genotype was 230.1 over all SSR markers, leading to an average of 2.3 alleles per SSR marker and genotype. The lowest number of alleles was found for genotype ID_25 with 191 alleles, the highest for ID_07 with 268 alleles. Assigning all individuals to cultivars and non-cultivars (ecotypes, landraces and NA) resulted in 981 alleles for individuals from cultivars (57.7 alleles per individual) and 942 alleles for non-cultivars (62.8 alleles per individual).

Diversity of O. viciifolia individuals

The allocation of individuals to groups by overall similarity of alleles was assessed using k-means partition comparisons. Those k-means statistic (Fig. 1, left) simulate a grouping of individuals (assigned by different colors) dependent on number of groups chosen. Individuals were assigned into two to ten groups, with a more homogenous grouping for two and three groups. The Calinski criterion (Fig. 1, right), giving the most likely grouping by the highest value reached, indicating a grouping of individuals into two groups by a value >3.
Fig. 1

Group separation of individuals as assessed by k-means partitioning for k = 2 to 10 with colors indicating different groups (left). The optimum number of groups (k) according to maximum Calinski criterion was determined to be two (right)

The cluster dendrogram based on the modified Roger’s distance (Fig. 2) also displayed a partitioning of individuals in two main groups, which were separated by a modified Roger’s distance value of 0.47. Individuals belonging to the same variety located in the same main branch for the varieties Perly (ID_14, ID_18; 0.4), Visnovsky (ID_13, ID_28; 0.39) and Zeus (ID_29, ID_30; 0.48). The variety Perdix is an advanced variety originating from the variety Perly and the Perdix genotype (ID_16) clusters closely to one of the Perly individuals (ID_14).
Fig. 2

Cluster Dendrogram of individuals based on the modified Rogers’ distance. Values at branches are AU p-values (blue). Different colours of genotype labels give the affiliation to the two groups determined by k-means partitioning

The first, smaller branch of the cluster (Fig. 2, right hand side) consisted mainly of individuals originating from Switzerland and the United Kingdom (cluster 1), whereas the majority of the second, larger branch was comprised of individuals from Southern and Eastern Europe as well as individuals from USA, Morocco and Canada (cluster 2). However, AU values showed no significance (values <95) for most branches. Principal component analysis (PCA; Fig. 3) showed a pattern comparable to that observed from cluster analysis with individuals of the two main clusters mainly being separated by the first principle component which explained 10.3 % of the total marker variation. The second principle component accounted for 4.9 % of the variation, most of which was intragroup. The occurrence of alleles across all markers varied between the two clusters with 849 alleles amplified in cluster 1 (65.3 per individual) and 979 alleles in cluster 2 (51.5 per individual).
Fig. 3

Principle component analysis of 32 sainfoin (O. viciifolia) individuals based on 1054 alleles of 101 SSR markers. Different colors give the affiliation to the two groups as determined by k-means partitioning

Discussion

The 101 SSR markers newly developed from sainfoin revealed a high degree of polymorphism. In addition to differences in multiples of the repeat motif, we also found alleles differing by fractions of the multiple motif length. Such variations could have arisen from insertions, deletions and translocations in the flanking region of the SSR [35]. Such mutations in the flanking region might also contribute to the high degree of polymorphism in our marker data set. The SSR sizes predicted through sequencing and the actual size distribution observed in the 32 individuals was consistent for most of the markers. Discrepancies can largely be explained by the fact that SSR motifs were developed from individuals not represented in the present study. In total, we found 1154 alleles at 101 loci resulting in 11.4 amplified alleles per SSR on average. This is twice the amount found by Demdoum [12], who found 5.83 alleles by transferring markers from barrel clover (Medicago truncatula Gaertn.) and soybean (Glycine max L.) to sainfoin. Fragments were smaller for the specific marker set in this study (92 to 511 bp) compared to markers adopted from other species [12] (79 to 865 bp). The larger sizes of alleles from cross-species amplification could be attributed to interspecific differences to the donor species due to repeat length variation within the SSR region and indels in the flanking region [36]. Avci [37] amplified 725 alleles from 18 SSR markers in diverse Onobrychis spp. using markers from pea and barrel clover. The higher number found by these authors could be explained by the larger diversity of germplasm used, which originated from different subspecies.

SSR marker studies with other tetraploid species using diverse panels of individuals showed lower numbers of alleles per marker compared to the present study, e.g. 7.2 alleles in sugar cane (Saccharum officinarum) [38], 6.7 alleles in switchgrass (Panicum virgatum) [39] and 6 alleles in peanut (Arachis hypogaea) [40].

A few markers were observed with less than five alleles among the 32 individuals. These may still be useful in future studies, since this study represents an initial screening of single individuals and not an extensive population survey. Additionally, using only the most polymorphic markers would bias the overall genetic diversity e.g. in conservation studies [41].

The challenge in analyzing SSR alleles in tetraploids lies in determining the dosage of each allele, which is often impossible using capillary electrophoresis for individuals carrying less than four different alleles at a specific marker locus. The PIC content gives an estimation of the information content of a marker and is traditionally calculated by the formula of Botstein [42]. This was developed for diploid species, for which the allele frequency is either known or can be inferred from the allele occurrence (presence/absence). For tetraploid species, the allele frequency is difficult to derive from the allele occurrence due to different allele doses (1 to 4 alleles). Hence, the formula for diploids could not be used for tetraploid sainfoin. Thus, the PIC was calculated separately for each allele, on the basis of allele occurrence counts, using a formula adopted from Roldan-Ruiz [26] and averaging the PIC across all loci of one locus [43]. Here, the maximum value that can be reached is 0.5, which corresponds to alleles found in 50 % of the population. Small values, on the other hand, correspond to very abundant or to very rare alleles. Deciding whether a SSR marker is useful also depends upon the scientific issue. Taking into account different allele-based PIC values of an SSR locus (Additional file 2: Figure S2), therefore, gives the most holistic picture of the SSR marker. High PIC values of alleles (0.5–0.4) are useful for inside population studies e.g. to trace marker trait associations, whereas low PIC values (0.0–0.1) of single alleles could be more useful for studies of evolution or genetic drift [44]. The average PIC values in this study indicated that most markers had alleles which could be found in a group of individuals and are suitable for several approaches in future studies. These PIC values were comparable to those found by Tehrani [43] which were between 0.16 and 0.44 in Lolium persicum Boiss. The large number of private alleles is a clear indication of genetic distinctness of the individuals, which was anticipated in view of their diverse origins.

Genetic diversity is a prerequisite for selection in variety development. So far, there is limited information on the genetic diversity of sainfoin available. Use of AFLP and SSR markers from other species were not able to reveal genetic diversity in distinct Spanish sainfoin accessions [12, 45]. The values of that study, given by Nei’s similarity values, which represent the proportion of shared fragments on the basis of binary data and corrected by the marker number [46], reached values of 0.73 to 0.8 [12, 45]. A conversion of those values to genetic distance values by the formula –ln (Nei’s similarity values) resulted in Nei’s genetic distance values of 0.31 and 0.22 [47]. In a study of sainfoin genetic diversity using RAPD markers in ten landraces from East Azerbaijan and in 36 Iranian sainfoin populations, Nei’s genetic distance values of 0.32 and 0.25, respectively, were observed [48, 49]. In our study, highest modified Roger’s distance of 0.48 corresponds to alleles not shared between our two cluster groups, which is almost 50 % (Fig. 2). The smallest Roger’s distance values with 0.35, corresponds to an approximate Nei’s distance value of 0.43 (Additional file 1: Figure S1), which is higher than the low values observed in other studies [12, 45, 48]. The majority of among-genotype comparisons showed higher values. The higher values of genetic diversity found in the present study may reflect the high variability of the markers developed and the selection of 32 individuals of contrasting origin. Despite the fact that individuals of the same cultivars in this predominantly outbreeding species can show considerable variability [50], the individuals from the same cultivar grouped clearly together in the present study (Figs. 2 and 3, Table 1).

The 32 individuals investigated separated into two clear groups based on different multivariate analyses. The first main group was comprised mainly of individuals from Switzerland and the United Kingdom, whereas the second group contained individuals originating from South and East Europe as well as USA, Canada and Morocco. In some instances, individuals originating from the same geographical region did not cluster tightly together, some even into the two different cluster groups. The three plants from Italy, ID_29 and ID_30, both cultivar “Zeus”, clustered in group 2, whereas ID_31 of the cultivar “Ambra” clustered to group 1). Especially for cultivars, this is likely to be due to different origin of base material (which is often unknown), as well as divergent breeding and selection history.

A similar grouping of accessions identified by the present cluster analysis could be found in earlier studies between sainfoin accessions from Western Europe and those from Eastern Europe and Asia [12, 23]. This clear genetic distinction between the individuals from Western Europe and those from Eastern Europe and beyond could reflect adaptation to diverse climatic conditions either naturally or as a result of local selection by growers [44]. Under genetic isolation and limited gene exchange, differentiation in the sainfoin germplasm with accompanied morphological separation seems likely [51]. The average number of alleles amplified in individuals of the West European cluster was 65.3, which was approximately 14 alleles more than individuals from the other cluster (51.5). These results might indicate a higher allelic diversity in individuals from mainly Switzerland and Great Britain compared to other origins. Deducing differences in tannin content and composition between single individuals of the two clusters based on earlier studies dealing with samples of plants from the same accessions is extremely difficult because the variation found within accessions is at least as large as variation between accessions [24].

Conclusions

This study reports the first characterization of specific co-dominant SSR markers for sainfoin. The 101 SSR markers characterized in this study showed a high degree of polymorphism and clearly demonstrated the differences between sainfoin individuals, with diverse origin, on a molecular genetic level. The genetic differences found in our panel separated the individuals into two groups, with a clear correlation to the geographical origin of those individuals. SSR markers, such as those characterized here, will be very useful in future genetic analyses, such as paternity or pedigree analysis in breeding programs, as well as more detailed analysis of genetic diversity in this forage crop. Furthermore, the development of new varieties could be crucially improved by choosing distinct genepools and minimising inbreeding depression.

Abbreviations

AFLP: 

Amplified fragment length polymorphism

EST-SSR: 

Expressed sequence tag – short sequence repeats

ITS: 

Internal transcribed spacer

RAPD: 

Random amplified polymorphic DNA

SNP: 

Single nucleotide polymorphism

SRAP: 

Sequence related amplified polymorphism

SSR: 

Short sequence repeats

Declarations

Acknowledgements

We wish to thank the European Commission for funding this research (Marie Curie Initial Training Network, ‘LegumePlus’, PITN-GA-2011-289377). The authors are grateful to Rhys Kelly and Kirsten Skøt (IBERS) and Tom Wood (NIAB) for help with the SSR analysis.

Funding

This research was funded by the European Commission through a Marie Curie Initial Training Network, ‘LegumePlus’, PITN-GA-2011-289377.

Availability of data and materials

The list of primers and accessions used are provided in the paper. The full list of all putative SSR loci and the sainfoin transcriptome sequence is provided in a manuscript currently under review, and is also available from the corresponding author.

Authors’ contributions

KK and MMO contributed to the experimental design, performed all analyses and led the writing of the manuscript. LMJS assisted in data collection, variety choice, structuring the work programme and helped to draft the manuscript. RK and LS conceived the study and assisted with data collection, data analysis and drafting the manuscript. All authors read and approved the final version of the manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

No ethics approval were needed for this work.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Agroscope ISS
(2)
National Institute of Agricutural Botany
(3)
IBERS, Aberystwyth University

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